Billiards and Tilts Analysis Notebook

0.1 Load all of the throw data

# load omnibus dataframe
omnibus_df <- read_delim("../data/processed/omnibus/omnibus_raw.csv",
  delim = ",",
  col_types = cols(
    .default = col_double(),
    type = col_factor(),
    ppid = col_factor(),
    exp_label = col_factor(),
    experiment = col_factor(),
    hand = col_factor(),
    camera_tilt = col_factor(),
    surface_tilt = col_factor(),
    target = col_factor(),
    test_type = col_factor(),
    prior_anim = col_factor(),
    baseline_block = col_factor(),
    task_type = col_factor(),
    surface = col_factor(),
    anim_type = col_factor()
  )
) %>% # filter out practice blocks
  filter(block_num > 4)

# Optionally make learning rate summaries
# do the following if learning_rate_df.csv doesn't exist in ../data/processed
# This takes a loong time
if (!file.exists("../data/processed/learning_rate_df.csv")) {
  print(Sys.time())
  apply_exponentialFit <- function(df) {
    df %>%
      summarise(
        ppid = first(ppid),
        experiment = first(experiment),
        test_type = first(test_type),
        exponentialFit = exponentialFit2(norm_throw_deviation, mode = test_type[1])
      )
    }
  
  block_init_learning_rates <- omnibus_df %>%
    filter(str_detect(test_type, "init")) %>%
    group_by(ppid, experiment, test_type) %>%
    group_split() %>%
    future_map(apply_exponentialFit) %>%
    bind_rows() %>%
    unnest(cols = c('exponentialFit'))
  
  print("done")
  print(Sys.time())

  write_csv(block_init_learning_rates, "../data/processed/learning_rate_df.csv")
} else {
  print("learning_rate_df.csv exists, loading from file")
  block_init_learning_rates <- read_csv("../data/processed/learning_rate_df.csv",
                                        col_types = cols(
                                          .default = col_double(), 
                                          experiment = col_factor(),
                                          test_type = col_factor()
                                          ))
}

[1] "2023-06-23 19:18:08 EDT"
[1] "done"
[1] "2023-06-23 19:20:27 EDT"

1 Visualizing data (univariate)

Vectors representing the throw velocity (trace 0) and the velocity applied to the ball (trace 1). The y dimention of the throw is essentially ignored (in reality there is a slight tilt added to account for the tilt of the surface).

test_ppt <- 3

test_df <- omnibus_df %>% filter(ppid == test_ppt)


trial <- 250
trial_df <- filter(test_df, trial_num == trial)

x <- trial_df$flick_velocity_x
y <- trial_df$flick_velocity_y
z <- trial_df$flick_velocity_z

x2 <- trial_df$flick_direction_x * -1
y2 <- trial_df$flick_direction_y * -1
z2 <- trial_df$flick_direction_z * -1

# plot both
p <- plot_ly(x = c(0, x), y = c(0, y), z = c(0, z), type = "scatter3d", mode = "lines") %>%
  add_trace(x = c(0, x2), y = c(0, y2), z = c(0, z2), type = "scatter3d", mode = "lines") %>%
  layout(scene = list(
    xaxis = list(title = "x", range = c(-2, 2)),
    yaxis = list(title = "y", range = c(-1, 3)),
    zaxis = list(title = "z", range = c(-1, 3))
  ))

# Render the plot
p

note: this is a rotated trial

1.0.1 Distribution of errors

# plot distribution of error_size
p <- ggplot(omnibus_df, aes(
  x = error_size,
  fill = type
)) +
  geom_histogram(binwidth = .5, alpha = .6) +
  theme_minimal() +
  theme(text = element_text(size = 11)) +
  scale_fill_manual(values = c("#f9982c", "#d40000")) +
  labs(x = "Error Size (cm)", y = "Count")

p

1.0.2 Distribution of throw angles

# plot distribution of error_size
p <- ggplot(omnibus_df, aes(
  x = throw_deviation,
  fill = type
)) +
  geom_histogram(binwidth = 1, alpha = .6) +
  theme_minimal() +
  theme(text = element_text(size = 11)) +
  scale_fill_manual(values = c("#f9982c", "#d40000")) +
  labs(
    x = "Throw Angle (°)", y = "Count"
  ) + # dashed lines at 0, -15, -30
  geom_vline(
    xintercept = c(0, -15, -30), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  ) + # ticks of 15 degrees
  scale_x_continuous(
    breaks = c(-30, 0, 30, -60, -90)
  )

p

2 Original Experiments

2.0.1 Plot ANGULAR DEVIATIONS (hand angles)

Note: Blues = Acceleration Perturbations

# rest of the exps
data_per_group <- omnibus_df %>%
  filter(exp_label == "original_exps" | exp_label == "curved_path") %>%
  group_by(experiment, test_type, trial_num) %>%
  summarise(
    mean_deviation = mean(throw_deviation),
    ci_deviation = vector_confint(throw_deviation),
    .groups = "drop"
  )

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = trial_num, y = mean_deviation, colour = experiment
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Throw Angle (°)"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, -30), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(-15), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# p <- p +
#   scale_y_continuous(
#     limits = c(-10, 35),
#     breaks = c(0, 15, 30),
#     labels = c(0, 15, 30)
#   ) +
#   scale_x_continuous(
#     limits = c(0, 180),
#     breaks = c(0, 60, 120, 180),
#     labels = c(0, 60, 120, 180)
#   )

# set font size to 11
p <- p +
  theme(text = element_text(size = 11))

# add confidence intervals and data points
p <- p + geom_ribbon(
  aes(
    ymin = mean_deviation - ci_deviation,
    ymax = mean_deviation + ci_deviation,
    fill = experiment
  ),
  colour = NA, alpha = 0.3
) + geom_line()

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)


# # save
# if (save_plots) {
#   ggsave(
#   p,
#   filename = "../plots/paper_figs/sr_30_training.pdf", device = "pdf",
#   height = 4, width = 6
#   )
#   }

ggplotly(p)

# p

2.0.2 Trial sets of interest only

Note: Blues = Acceleration Perturbations

# filter out just the trials of interest
data_per_group <- data_per_group %>%
  filter(
    test_type != "other"
  )
# add a dummy column with repeating sequence
# NOTE: this can't be combined with above since we are using nrow
data_per_group <- data_per_group %>%
  mutate(dummy_x = rep(1:(nrow(data_per_group) / NUM_EXPS),
    length.out = nrow(data_per_group)
  ))

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = dummy_x, y = mean_deviation, colour = experiment
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Throw Angle (°)"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, -30), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(-15), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# add confidence intervals and data points
for (unique_test_type in unique(data_per_group$test_type)) {
  # get the data for this block
  to_plot_data <- filter(data_per_group, test_type == unique_test_type)

  p <- p + geom_ribbon(
    data = to_plot_data,
    aes(
      ymin = mean_deviation - ci_deviation,
      ymax = mean_deviation + ci_deviation,
      fill = experiment
    ), colour = NA, alpha = 0.3
  ) + geom_line(
    data = to_plot_data
  )
}

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

2.0.3 Plot NORMALIZED ANGULAR DEVIATIONS (hand angles)

Note: Blues = Acceleration Perturbations

# rest of the exps
data_per_group <- omnibus_df %>%
  filter(exp_label == "original_exps" | exp_label == "curved_path") %>%
  group_by(experiment, test_type, trial_num) %>%
  summarise(
    mean_deviation = mean(norm_throw_deviation),
    ci_deviation = vector_confint(norm_throw_deviation),
    .groups = "drop"
  )

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = trial_num, y = mean_deviation, colour = experiment
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Normalized Throw Angle"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, 1, 2), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(0.5, 1.5), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# p <- p +
#   scale_y_continuous(
#     limits = c(-10, 35),
#     breaks = c(0, 15, 30),
#     labels = c(0, 15, 30)
#   ) +
#   scale_x_continuous(
#     limits = c(0, 180),
#     breaks = c(0, 60, 120, 180),
#     labels = c(0, 60, 120, 180)
#   )

# set font size to 11
p <- p +
  theme(text = element_text(size = 11))

# add confidence intervals and data points
p <- p + geom_ribbon(
  aes(
    ymin = mean_deviation - ci_deviation,
    ymax = mean_deviation + ci_deviation,
    fill = experiment
  ),
  colour = NA, alpha = 0.3
) + geom_line()

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)


# # save
# if (save_plots) {
#   ggsave(
#   p,
#   filename = "../plots/paper_figs/sr_30_training.pdf", device = "pdf",
#   height = 4, width = 6
#   )
#   }

ggplotly(p)

# p

2.0.4 Trial sets of interest only

Note: Blues = Acceleration Perturbations

# filter out just the trials of interest
data_per_group <- data_per_group %>%
  filter(
    test_type != "other"
  )
# add a dummy column with repeating sequence
# NOTE: this can't be combined with above since we are using nrow
data_per_group <- data_per_group %>%
  mutate(dummy_x = rep(1:(nrow(data_per_group) / NUM_EXPS),
    length.out = nrow(data_per_group)
  ))

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = dummy_x, y = mean_deviation, colour = experiment
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Normalized Throw Angle"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, 1, 2), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(0.5, 1.5), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# add confidence intervals and data points
for (unique_test_type in unique(data_per_group$test_type)) {
  # get the data for this block
  to_plot_data <- filter(data_per_group, test_type == unique_test_type)

  p <- p + geom_ribbon(
    data = to_plot_data,
    aes(
      ymin = mean_deviation - ci_deviation,
      ymax = mean_deviation + ci_deviation,
      fill = experiment
    ), colour = NA, alpha = 0.3
  ) + geom_line(
    data = to_plot_data
  )
}

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

2.1 Learning Rates, Start Points and Asymptotes

2.1.1 Learning Rates

data_ppt <- block_init_learning_rates %>%
  filter(experiment != "a_ball_roll_animate_surface")

data_group <- data_ppt %>%
  group_by(experiment, test_type) %>%
  summarise(
    mean_learning_rate = mean(exp_fit_lambda),
    ci_learning_rate = vector_confint(exp_fit_lambda),
    mean_high = mean(exp_fit_N0),
    ci_high = vector_confint(exp_fit_N0),
    mean_low = mean(exp_fit_displace),
    ci_low = vector_confint(exp_fit_displace),
    .groups = "drop"
  )

p <- data_group %>%
  ggplot(
    aes(x = experiment, y = mean_learning_rate, colour = experiment)
  ) +
  theme_classic() +
  labs(
    x = NULL,
    y = "Learning Rate"
  ) +
  facet_wrap(~test_type)

# remove all x axis labels
p <- p + theme(axis.text.x = element_blank())

# for the colour legend, only show the first 7 Note this doesn't work for the plotly plot
p <- p + guides(colour = guide_legend(override.aes = list(alpha = 1)))

# add data points
p <- p +
  geom_beeswarm(
    data = data_ppt,
    aes(
      y = exp_fit_lambda
    ),
    alpha = 0.1,
    size = 1
  ) +
  geom_linerange(aes(
    ymin = mean_learning_rate - ci_learning_rate,
    ymax = mean_learning_rate + ci_learning_rate
  ), alpha = 0.5, lwd = 2) +
  geom_point()

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

# p

2.1.2 High Points (Asymptotes or Starts)

p <- data_group %>%
  ggplot(
    aes(x = experiment, y = mean_high, colour = experiment)
  ) +
  theme_classic() +
  labs(
    x = NULL,
    y = "High Point"
  ) +
  facet_wrap(~test_type)

# remove all x axis labels
p <- p + theme(axis.text.x = element_blank())

# for the colour legend, only show the first 7 Note this doesn't work for the plotly plot
p <- p + guides(colour = guide_legend(override.aes = list(alpha = 1)))

# add data points
p <- p +
  geom_beeswarm(
    data = data_ppt,
    aes(
      y = exp_fit_N0
    ),
    alpha = 0.1,
    size = 1
  ) +
  geom_linerange(aes(
    ymin = mean_high - ci_high,
    ymax = mean_high + ci_high
  ), alpha = 0.5, lwd = 2) +
  geom_point()

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

# p

2.1.3 Low Points (Asymptotes or Starts)

### Low Points (Asymptotes or Starts)

p <- data_group %>%
  ggplot(
    aes(x = experiment, y = mean_low, colour = experiment)
  ) +
  theme_classic() +
  labs(
    x = NULL,
    y = "Low Point"
  ) +
  facet_wrap(~test_type)

# remove all x axis labels
p <- p + theme(axis.text.x = element_blank())

# for the colour legend, only show the first 7 Note this doesn't work for the plotly plot
p <- p + guides(colour = guide_legend(override.aes = list(alpha = 1)))

# add data points
p <- p +
  geom_beeswarm(
    data = data_ppt,
    aes(
      y = exp_fit_displace
    ),
    alpha = 0.1,
    size = 1
  ) +
  geom_linerange(aes(
    ymin = mean_low - ci_low,
    ymax = mean_low + ci_low
  ), alpha = 0.5, lwd = 2) +
  geom_point()

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

# p

For washout: The CUED accel + curved have a lower starting point (therefore – cue works). VMR group has slightly lower. When comparing everything with a high starting point, the ACCEL group has a much FASTER learning rate. When transferring, no difference in learning rates.

# Compare learning rates and high points
p <- data_ppt %>% 
  ggplot(
    aes(x = exp_fit_lambda, y = exp_fit_N0, colour = experiment)
  ) +
  theme_classic() +
  labs(
    x = "Learning Rate",
    y = "High Point"
  ) +
  facet_wrap(~test_type) +
  geom_point() +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

2.1.4 Correlations within the 3 variables above

# 3d plot of learning rates, high points, and low points
# filter out just the washout trials for now
data_ppt <- block_init_learning_rates %>%
  filter(test_type == "washout_init")

p <- plot_ly(type = 'scatter3d', mode = 'markers')

for (experiment_ in unique(data_ppt$experiment)) {
  # get the data for this block
  to_plot_data <- filter(data_ppt, experiment == experiment_)
  
  p <- p %>%
    add_trace(
      x = to_plot_data$exp_fit_lambda, 
      y = to_plot_data$exp_fit_N0, 
      z = to_plot_data$exp_fit_displace, 
      color = I(pallete_list[experiment_]),
      name = experiment_
    )
}

p

• We might want to test if data points cluster differently depending on the experiment conditions.
• Note: It is a good idea to do some EDA to check if all 3 dimensions are required. Correlation matrix?

### TESTING
# rest of the exps
data_ <- omnibus_df %>%
  filter(experiment == "accel_uncued", test_type == "washout_init")
  
# set up plot
p <- data_ %>%
  ggplot(
    aes(
      x = trial_num, y = norm_throw_deviation, colour = ppid
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Normalized Throw Angle"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, 1, 2), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(0.5, 1.5), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# p <- p +
#   scale_y_continuous(
#     limits = c(-10, 35),
#     breaks = c(0, 15, 30),
#     labels = c(0, 15, 30)
#   ) +
#   scale_x_continuous(
#     limits = c(0, 180),
#     breaks = c(0, 60, 120, 180),
#     labels = c(0, 60, 120, 180)
#   )

# set font size to 11
p <- p +
  theme(text = element_text(size = 11))

# add confidence intervals and data points
p <- p + geom_line()



# # save
# if (save_plots) {
#   ggsave(
#   p,
#   filename = "../plots/paper_figs/sr_30_training.pdf", device = "pdf",
#   height = 4, width = 6
#   )
#   }

ggplotly(p)

# p

2.2 Plot ERROR SIZE

Note: Blues = Acceleration Perturbations

# original experiments only
data_per_group <- omnibus_df %>%
  filter(exp_label == "original_exps" | exp_label == "curved_path") %>%
  group_by(experiment, test_type, trial_num) %>%
  summarise(
    mean_deviation = mean(error_size),
    ci_deviation = vector_confint(error_size),
    .groups = "drop"
  )

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = trial_num, y = mean_deviation, colour = experiment
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Absolute Target Error (cm)"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, 40), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(20), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# p <- p +
#   scale_y_continuous(
#     limits = c(-10, 35),
#     breaks = c(0, 15, 30),
#     labels = c(0, 15, 30)
#   ) +
#   scale_x_continuous(
#     limits = c(0, 180),
#     breaks = c(0, 60, 120, 180),
#     labels = c(0, 60, 120, 180)
#   )

# set font size to 11
p <- p +
  theme(text = element_text(size = 11))

# add confidence intervals and data points
p <- p + geom_ribbon(
  aes(
    ymin = mean_deviation - ci_deviation,
    ymax = mean_deviation + ci_deviation,
    fill = experiment
  ),
  colour = NA, alpha = 0.3
) + geom_line()

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)


# # save
# if (save_plots) {
#   ggsave(
#   p,
#   filename = "../plots/paper_figs/sr_30_training.pdf", device = "pdf",
#   height = 4, width = 6
#   )
#   }

ggplotly(p)

# p

visible vs non-visible tilt doesn’t affect the 15-degree rotation condition. But affects all other conditions. So 15-degree rotation

2.2.1 Trial sets of interest only

Note: Blues = Acceleration Perturbations

# filter out just the trials of interest
data_per_group <- data_per_group %>%
  filter(
    test_type != "other"
  )
# add a dummy column with repeating sequence
# NOTE: this can't be combined with above since we are using nrow
data_per_group <- data_per_group %>%
  mutate(dummy_x = rep(1:(nrow(data_per_group) / NUM_EXPS),
    length.out = nrow(data_per_group)
  ))

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = dummy_x, y = mean_deviation, colour = experiment
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Absolute Target Error (cm)"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, 40), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(20), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# add confidence intervals and data points
for (unique_test_type in unique(data_per_group$test_type)) {
  # get the data for this block
  to_plot_data <- filter(data_per_group, test_type == unique_test_type)

  p <- p + geom_ribbon(
    data = to_plot_data,
    aes(
      ymin = mean_deviation - ci_deviation,
      ymax = mean_deviation + ci_deviation,
      fill = experiment
    ), colour = NA, alpha = 0.3
  ) + geom_line(
    data = to_plot_data
  )
}

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

3 Animated Surface Follow-up

3.0.1 Plot ANGULAR DEVIATIONS (hand angles)

# isolate animate_surface exp
data_per_group <- omnibus_df %>%
  filter(exp_label == "animate_surface") %>%
  group_by(prior_anim, block_num, trial_num_in_block, trial_num) %>%
  summarise(
    mean_deviation = mean(throw_deviation),
    ci_deviation = vector_confint(throw_deviation)
  )

# order the factors for assigning colour pallets
data_per_group$prior_anim <- factor(
  data_per_group$prior_anim,
  levels = c(
    "none", "half_anim", "full_anim"
  )
)

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = trial_num, y = mean_deviation,
      ymin = mean_deviation - ci_deviation,
      ymax = mean_deviation + ci_deviation
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number",
    y = "Throw Angle (°)"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, -30), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(-15), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# p <- p +
#   scale_y_continuous(
#     limits = c(-10, 35),
#     breaks = c(0, 15, 30),
#     labels = c(0, 15, 30)
#   ) +
#   scale_x_continuous(
#     limits = c(0, 180),
#     breaks = c(0, 60, 120, 180),
#     labels = c(0, 60, 120, 180)
#   )

# set font size to 11
p <- p +
  theme(text = element_text(size = 11))

# repeat for prior_anim == "half", "full" and "wait"
for (unique_prior_anim in unique(data_per_group$prior_anim)) {
  # get the data for this block
  to_plot_data <- filter(data_per_group, prior_anim == unique_prior_anim)
  # loop through the unique blocks in to_plot_data
  for (block in unique(to_plot_data$block_num)) {
    # get the data for this block
    block_data <- filter(to_plot_data, block_num == block)
    # add the data, use the pallete_list to get the colour
    p <- p + geom_ribbon(
      data = block_data,
      aes(fill = prior_anim),
      colour = NA, alpha = 0.3
    ) + geom_line(
      data = block_data,
      aes(colour = prior_anim)
    )
  }
}

# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

# # save
# if (save_plots) {
#   ggsave(
#   p,
#   filename = "../plots/paper_figs/sr_30_training.pdf", device = "pdf",
#   height = 4, width = 6
#   )
#   }

ggplotly(p)

# p

3.0.2 Washout trials after half VS full animations

# first, isolate the data
data <- omnibus_df %>%
  filter(
    exp_label == "animate_surface",
    baseline_block == FALSE,
    test_type == "washout_anim"
  )

data_per_ppt <- data %>%
  group_by(ppid, prior_anim, trial_num_in_block) %>%
  summarise(
    ppt_mean_deviation = median(throw_deviation),
    ppt_ci_deviation = vector_confint(throw_deviation),
    n = n()
  )

data_per_group <- data_per_ppt %>%
  group_by(prior_anim, trial_num_in_block) %>%
  summarise(
    mean_deviation = mean(ppt_mean_deviation),
    ci_deviation = vector_confint(ppt_mean_deviation),
    n = sum(n)
  )

# set up plot
p <- data_per_group %>%
  ggplot(
    aes(
      x = trial_num_in_block, y = mean_deviation,
      colour = prior_anim, fill = prior_anim
    )
  ) +
  theme_classic() +
  # theme(legend.position = "none") +
  labs(
    x = "Trial Number in Block",
    y = "Throw Angle (°)"
  )

# add horizontal lines
p <- p +
  geom_hline(
    yintercept = c(0, -30), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "solid"
  ) +
  geom_hline(
    yintercept = c(-15), linewidth = 0.4,
    colour = "#CCCCCC", linetype = "dashed"
  )

# add data points
p <- p + geom_beeswarm(
  data = data_per_ppt,
  aes(
    y = ppt_mean_deviation,
    colour = prior_anim
  ),
  size = 1, dodge.width = 0.5
) + geom_ribbon(
  aes(
    ymin = mean_deviation - ci_deviation,
    ymax = mean_deviation + ci_deviation
  ),
  colour = NA, alpha = 0.3
) + geom_line()


# set colour palette
p <- p +
  scale_colour_manual(values = pallete_list) +
  scale_fill_manual(values = pallete_list)

ggplotly(p)

4 Deprecated (Don’t Run)

4.1 Success manifolds

4.1.1 Without any tilts

# ggplotly(plot_success_manifold_no_tilt())
plot_success_manifold_no_tilt()

4.1.2 With tilt present

ggplotly(plot_success_manifold_tilt())